On the Design of Large-Scale UMTS Mobile Networks Using Hybrid Genetic Algorithms
Bibliographic record
Abstract
Third-generation mobile systems provide access to a wide range of services and enable mobile users to communicate, regardless of their geographical location and their roaming characteristics. Due to the growing number of mobile users and global connectivity, one of the most critical issues regarding the design of universal mobile telecommunications service (UMTS) networks pertains to the assignment of Node Bs to radio network controllers (RNCs), which is an NP-hard problem. Hence, for real-sized mobile networks, this problem cannot be practically solved by using exact methods. This paper proposes a hybrid genetic algorithm (HA) with migration to solve the problem of assigning Bs to RNCs as a design step of large-scale UMTS mobile networks. Computational results obtained from extensive tests confirm the effectiveness of the HA to provide superior solutions compared to other heuristic methods that are well documented in the literature. Such an algorithm is particularly suitable to design large-scale cellular mobile networks with Node Bs whose quantity varies between 100 and 500 and whose the number of RNCs ranges between five and ten.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".